The admission pH is a risk factor of preoperative deep vein thrombosis in geriatric hip fracture: a retrospective cohort study

This study evaluated the association between body pH value and preoperative deep vein thrombosis (DVT) in geriatric hip fractures. Older adult patients with hip fractures were screened between January 2015 and September 2019. The demographic and clinical characteristics of the patients were collected. Multivariate binary logistic regression and generalized additive models were used to identify the linear and nonlinear associations between pH value and preoperative DVT. Analyses were performed using EmpowerStats and R software. A total of 1465 patients were included in the study. DVT occurred in 476 (32.6%) of these admitted older adults. We observed a nonlinear association between the serum pH value and preoperative DVT in geriatric patients with hip fractures. A pH value of 7.39 was the inflection point in the curve, with pH highly correlated with DVT at pH < 7.39 (odds ratio [OR] 19.47; 95% confidence interval [CI] 1.45–260.91; P = 0.0249). Patients with lower pH had a lower chance of preoperative DVT formation, and the risk of DVT increased 18.47-fold for every 0.1 unit change in pH. Although at pH > 7.39, pH was not correlated with DVT (OR 1.26; 95% CI 0.85–1.86; P = 0.2561), the odds of DVT did not vary with pH, and the highest risk of thrombosis was reached. The body pH value is nonlinearly associated with preoperative DVT in geriatric patients with hip fractures, and it could be considered a predictor of the risk of DVT. Registered information This study is registered in the website of Chinese Clinical Trial Registry (ChiCTR: ChiCTR2200057323).


Participants
The demographic and clinical data of the patients were obtained from their original medical records.The inclusion criteria were as follows: (1) age of ≥ 65 years; (2) X-ray or computed tomography diagnosis of the femoral neck, intertrochanteric, or subtrochanteric fracture; (3) patients who were receiving surgical or conservative treatment in the hospital; and (4) availability of clinical data in the hospital.Exclusion criteria: patients who did not receive the pharmacological thromboprophylaxis because of contraindications.

Hospital treatment
Patients were examined using blood tests to prepare for surgery.Prophylaxis for DVT was initiated on admission.Mechanical thromboprophylaxis (foot intermittent pneumatic compression sleeve, 20 min twice daily) was used to prevent DVT.In patients without contraindications, low-molecular-weight heparin was injected subcutaneously to prevent DVT.Doppler ultrasonography was performed to diagnose DVT by two senior sonographers (Hong Zhang, Yi-Lun Wu).The diagnostic criterion was the presence of a constant intraluminal filling defect.All patients were examined preoperatively.All patients underwent ultrasonography of the bilateral lower extremities the day before the scheduled surgery.

Endpoint events
The endpoint event in this study was DVT before operation.

Variables
The variables collected in this study were as follows: age, sex, occupation, history of allergy, injury mechanism, fracture classification, hypertension, diabetes, coronary heart disease, arrhythmia, hemorrhagic stroke, ischemic stroke, cancer, associated injuries, dementia, chronic obstructive pulmonary disease, hepatitis, gastritis, ageadjusted Charlson comorbidity index (aCCI), time from injury to admission, blood predictors and pH value.
The dependent variable was preoperative DVT, and the independent variable was the pH value.The other variables were potentially confounding factors.

Statistics analysis
Continuous variables were reported as mean ± standard deviation (Gaussian distribution) or median (minimum and maximum) (skewed distribution), and categorical variables were presented as frequencies and percentages.We used χ2 (categorical variables), one-way ANOVA (normal distribution), or the Kruskal-Wallis H test (skewed distribution) to test for differences among different pH values (quartile).Using three distinct models, we used univariate and multivariate binary logistic regression models to test the association between pH and preoperative DVT.Model 1 was a non-adjusted model with no covariates adjusted for.Model 2 was a minimally adjusted model with adjusted sociodemographic variables.Model 3 was a fully adjusted model with the covariates presented in Table 1.To account for the nonlinear relationship between pH and preoperative DVT, we also used a generalized additive model and smooth curve fitting (penalized spline method) to address nonlinearity.In addition, a two-piecewise binary logistic regression model was used to explain nonlinearity further.To test the robustness of our results, we performed a sensitivity analysis.We converted pH into a categorical variable according to the quartile.We calculated the P for the trend to verify the results of pH as a continuous variable and examine the possibility of nonlinearity.In addition, propensity score matching (PSM) was introduced for matched groups and adjusted for confounding factors in the PSM models.

Ethics approval and consent to participate
The study was approved by the Ethics Committee of the Honghui Hospital, Xi'an Jiaotong University (No. 202201009).

Consent to publish
The work described has not been published before (except in the form of an abstract or as part of a published lecture, review, or thesis); it is not under consideration for publication elsewhere; and its publication has been approved by all co-authors.

Patient characteristics
In this retrospective cohort study, we screened older adults who had hip fractures in the hospital from January 1, 2015, to September 30, 2019, and recruited 1470 study participants who met the inclusion criteria.Five patients were excluded because of pharmacological thromboprophylaxis contraindications according to exclusion criteria.A total of 1465 patients were included in the study.Preoperative DVT occurred in 476 (32.6%) admitted older adults.Table 1 lists the demographic and clinical characteristics of the study population after dividing the pH tertiles into the low, middle, and high groups.

Univariate analysis of the association between variates and DVT
We performed a univariate analysis to identify the relationship between the study factors and DVT.The results in Table 2 show differences in DVT according to sex, fracture classification, multiple injuries, dementia, gastritis, time to operation, and pH.To more accurately investigate the relationship between pH and DVT, confounding factors were identified at P < 0.1.Sex, fracture classification, multiple injuries, time to operation, dementia, gastritis, and activated partial thromboplastin time were identified as confounding factors.

Multivariate analysis between preoperative pH and DVT
To demonstrate the relationship between pH and DVT in patients, data in Table 3 were further adjusted for confounding factors to perform multifactorial logistic regression.In addition, pH was tested for trend as a categorical www.nature.com/scientificreports/variable (in three groups by trichotomization) and as a continuous variable.The results were statistically significant when pH was used as a continuous variable.In contrast, there was no statistical difference between the two results when divided into three groups and in the trend test calculated as a continuous variable.Therefore, the results were unstable, and other relationships were considered possible, most often curvilinear.

Curve fitting and analysis of threshold effect
A curve fit in Fig. 1 revealed a possible curvilinear relationship; therefore, an attempt was made to identify the curvilinear relationship using Table 4.The result was that there was indeed a curvilinear relationship, that is, the curvilinear relationship held in the current study.A pH value of 7.39 was the inflection point in the curve, and at pH < 7.39, pH was highly correlated with DVT (OR 19.47; 95% CI 1.45-260.91;P = 0.0249), whereas at pH > 7.39, pH was not associated with DVT (OR 1.26; 95% CI 0.85-1.86;P = 0.2561).

Propensity score matching
To test the robustness of our results, we performed sensitivity analysis using PSM, as shown in Fig. 2 and Tables 5-7.Eight hundred eighty patients (60.06%) were successfully matched (Fig. 2 and Table 5).Sex, fracture classification, diabetes, hemorrhagic stroke, ischemic stroke, multiple injuries, time to operation, and aCCI did not match between the two groups (Table 6).We found that the results were stable in the multivariate regression results under the PSM and PSM-adjusted models (Table 7).The pH value was 7.39, the curve's inflection point.

Discussion
We observed a linear association between serum pH value and preoperative DVT in geriatric patients with hip fractures.pH is a risk factor for DVT formation; however, its relationship is curvilinear.A pH value of 7.39 was the inflection point in the curve, with pH highly correlated with DVT at pH < 7.39 (OR 19.47; 95% CI 1.45-260.91;P = 0.0249).Patients with lower pH had a lower chance of preoperative DVT formation, and the risk of DVT increased 18.47-fold for every 0.1 unit change in pH.Although at pH > 7.39, pH was not correlated with DVT (OR 1.26; 95% CI 0.85-1.86;P = 0.2561), the odds of DVT did not vary with pH, and the highest risk   The accepted pathogenesis of lower limb DVT is based on the three main elements of DVT formation proposed by Virchow in 1856: slow blood flow, intimal damage, and hypercoagulable blood 20 .Although most hip fractures in the elderly are caused by low-energy injuries, with high-energy injuries accounting for a relatively low percentage of injuries 21 , hip injuries have a more significant impact on the systemic condition of elderly patients, especially in terms of hemodynamic instability and imbalance in the homeostasis of the internal environment, which is more likely to stimulate exogenous coagulation mechanisms and increase the risk of DVT formation 22 .
DVT formation is a complex and dynamic physiological process, and susceptibility to DVT thromboembolism is associated with its metabolites 23 .The most commonly described metabolites possibly related to DVT are carnitine species, glucose, phenylalanine, 3-hydroxybutyrate, lactic acid, tryptophan, and some monounsaturated and polyunsaturated fatty acids 14 .Studies have shown that lactate levels can affect coagulation or platelet aggregation in all of these metabolites.Most elderly patients often have hidden blood loss after hip fracture 24 .Acute blood loss leads to decreased cardiac output, tachycardia, hypotension, and inadequate organ perfusion, which interferes with aerobic metabolism, and increased anaerobic metabolism leads to the production of lactic acid and metabolic acidosis.In addition, an increase or decrease in pH value can lead to disturbances in the internal environment.A hypoxic microenvironment at the fracture site is created by reduced or blocked blood supply, and anaerobic enzymes are enhanced, resulting in lactic acid accumulation, thus creating an acidic microenvironment with a low pH value at the fracture site 25,26 .Elevated lactate levels are thought to be associated with enhanced glycolysis in erythrocytes within the thrombus.A study by Maekawa et al. 27 found lactate was the most abundant metabolite in the thrombus, with levels at least five times higher than those in venous blood.As Crowell et al. demonstrated, lactic acid stimulated whole blood clotting and impeded platelet aggregation to some extent.As the pH decreased, lactic acid infusion was significantly associated with a reduction in whole blood clotting time in vitro and in vivo 28 .In addition, the acid-base imbalance can damage the vascular endothelium, and damaged endothelial cells can release tissue factors, which activate the blood barrier system, reduce fibrinolytic activity, and enhance platelet adhesion and aggregation.The potential impact of an acidic environment with low pH values on cell function is essential 29 .A decrease in blood pH can lead to an increase in the enzymatic activity of coagulation factors, a reduction in the anticoagulant activity of heparin, and an increase in the ability to aggregate platelets, leading to hypercoagulation of the blood and, ultimately, DVT.Therefore, we speculate that the correlation between pH and DVT is local lactic acid accumulation in trauma caused by blood loss, which changes the pH value of the internal environment and affects the normal metabolic environment through several pathways, leading to the formation of DVT.However, this hypothesis should be tested in future studies.
In the current study, we performed univariate analysis to derive a more reliable relationship between admission pH and DVT and identified confounding factors for DVT formation.Sex, fracture classification, multiple injuries, time to operation, dementia, gastritis and activated partial thromboplastin time were identified as confounding factors.Multifactorial logistic regression was performed after further adjustment for confounding variables, and the results were found to be statistically significant when pH was used as a continuous variable but not in the trend test.A curvilinear relationship between pH and DVT was inferred using curve fitting.A pH value of 7.39 was found to be the inflection point in the curve.
This study is the first to explore the relationship between pH at admission and DVT formation.A curvilinear relationship was found between the two factors for the first time.Regarding other blood data (D-dimer, fibrinogen, activated partial thromboplastin time, prothrombin time, thrombin time, albumin, hemoglobin, total cholesterol, triglycerides), a meta-analysis reported no significant differences in these parameters 30 .This underscores the importance of pH as a predictor of DVT.
This study has some limitations.First, this retrospective study explored the relationship between admission pH and DVT formation.However, this study could not determine a causal relationship between these two factors.Second, our study population was from the largest trauma center in Northwest China.There may have been some bias in the selection of the study population due to region, ethnicity, and lifestyle habits.This results in findings that cannot be generalized to the entire population.All of these shortcomings need to be further confirmed in future studies.
In conclusion, the pH value is nonlinearly associated with preoperative DVT in geriatric patients with hip fractures and could be considered a predictor of the risk of DVT.

Table 1 .
The demographic and clinical characteristics.Mean + standard deviation/Median (min-max)/N(%).P value*: For continuous variables, we used the Kruskal-Wallis rank-sum test and Fisher's exact probability test for count variables with a theoretical number of < 10.

Table 2 .
Effects of factors on DVT measured by univariate analysis.

Table 3 .
The multivariate results by linear regression.Data in the table: OR (95% CI) P value.Outcome variable: deep vein thrombosis.Exposed variables pH.The minimally adjusted model was adjusted for sex.The fully adjusted model was adjusted for sex, fracture classification, multiple injuries, time to operation, dementia, gastritis and activated partial thromboplastin time.
Figure 1.Curve fitting between preoperative pH and deep vein thrombosis (DVT).Adjusted for sex, fracture classification, multiple injuries, time to operation, dementia, gastritis and activated partial thromboplastin time.

Table 5 .
Propensity score parameter list.

Table 6 .
The balance test of propensity score matching (N = 880).*Variables were not successfully matched.

Table 7 .
19ltivariate According to the comparison of logistic regression and PSM results, the logistic regression was more accurate19in this study.Indeed, the results of logistic regression and PSM results have the same inflection point in this study.
results by the regression analysis.Data in the table: OR (95% CI) P value.Outcome variable: deep vein thrombosis.Exposed variables: pH.Adjusted variables in the propensity score matching (PSM)-adjusted model: sex, fracture classification, diabetes, hemorrhagic stroke, ischemic stroke, multiple injuries, and time to operation, and aCCI.